
"""simple docstring"""






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}



class 			lowercase					( lowercase__		):
			lowercase        =     '''gptj'''
			lowercase        =     {
			    '''max_position_embeddings''': '''n_positions''',
			    '''hidden_size''': '''n_embd''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}






			def __init__(self  :	int      ,SCREAMING_SNAKE_CASE_  :	Dict=50_400      ,SCREAMING_SNAKE_CASE_  :	List[str]=2_048      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=4_096      ,SCREAMING_SNAKE_CASE_  :	str=28      ,SCREAMING_SNAKE_CASE_  :	str=16      ,SCREAMING_SNAKE_CASE_  :	int=64      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=None      ,SCREAMING_SNAKE_CASE_  :	str="gelu_new"      ,SCREAMING_SNAKE_CASE_  :	Dict=0.0      ,SCREAMING_SNAKE_CASE_  :	str=0.0      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=0.0      ,SCREAMING_SNAKE_CASE_  :	Tuple=1e-5      ,SCREAMING_SNAKE_CASE_  :	Any=0.02      ,SCREAMING_SNAKE_CASE_  :	int=True      ,SCREAMING_SNAKE_CASE_  :	int=50_256      ,SCREAMING_SNAKE_CASE_  :	Any=50_256      ,SCREAMING_SNAKE_CASE_  :	Any=False      ,**SCREAMING_SNAKE_CASE_  :	str      ,)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  vocab_size
				lowerCAmelCase												=  n_positions
				lowerCAmelCase												=  n_embd
				lowerCAmelCase												=  n_layer
				lowerCAmelCase												=  n_head
				lowerCAmelCase												=  n_inner
				lowerCAmelCase												=  rotary_dim
				lowerCAmelCase												=  activation_function
				lowerCAmelCase												=  resid_pdrop
				lowerCAmelCase												=  embd_pdrop
				lowerCAmelCase												=  attn_pdrop
				lowerCAmelCase												=  layer_norm_epsilon
				lowerCAmelCase												=  initializer_range
				lowerCAmelCase												=  use_cache

				lowerCAmelCase												=  bos_token_id
				lowerCAmelCase												=  eos_token_id

				super().__init__(
				    bos_token_id=SCREAMING_SNAKE_CASE_      ,eos_token_id=SCREAMING_SNAKE_CASE_      ,tie_word_embeddings=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)





class 			lowercase					( lowercase__		):






			def __init__(self  :	str      ,SCREAMING_SNAKE_CASE_  :	PretrainedConfig      ,SCREAMING_SNAKE_CASE_  :	str = "default"      ,SCREAMING_SNAKE_CASE_  :	List[PatchingSpec] = None      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,)       ->	Union[str, Any]:





				"""simple docstring"""


				super().__init__(SCREAMING_SNAKE_CASE_      ,task=SCREAMING_SNAKE_CASE_      ,patching_specs=SCREAMING_SNAKE_CASE_      ,use_past=SCREAMING_SNAKE_CASE_			)
				if not getattr(self._config      ,'''pad_token_id'''      ,SCREAMING_SNAKE_CASE_			):
					# TODO: how to do that better?
					lowerCAmelCase												=  0






			@property
			def 					UpperCAmelCase							(self  :	str			)       ->	Mapping[str, Mapping[int, str]]:





				"""simple docstring"""


				lowerCAmelCase												=  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}			)
				if self.use_past:
					self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_      ,direction='''inputs'''			)
					lowerCAmelCase												=  {0: '''batch''', 1: '''past_sequence + sequence'''}
				else:
					lowerCAmelCase												=  {0: '''batch''', 1: '''sequence'''}

				return common_inputs






			@property
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	int:





				"""simple docstring"""


				return self._config.n_layer






			@property
			def 					UpperCAmelCase							(self  :	Dict			)       ->	int:





				"""simple docstring"""


				return self._config.n_head






			def 					UpperCAmelCase							(self  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	PreTrainedTokenizer      ,SCREAMING_SNAKE_CASE_  :	int = -1      ,SCREAMING_SNAKE_CASE_  :	int = -1      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	Optional[TensorType] = None      ,)       ->	Mapping[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  super(SCREAMING_SNAKE_CASE_      ,self			).generate_dummy_inputs(
				    SCREAMING_SNAKE_CASE_      ,batch_size=SCREAMING_SNAKE_CASE_      ,seq_length=SCREAMING_SNAKE_CASE_      ,is_pair=SCREAMING_SNAKE_CASE_      ,framework=SCREAMING_SNAKE_CASE_			)

				# We need to order the input in the way they appears in the forward()
				lowerCAmelCase												=  OrderedDict({'''input_ids''': common_inputs['''input_ids''']}			)

				# Need to add the past_keys
				if self.use_past:
					if not is_torch_available():
						raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''			)
					else:
						import torch

						lowerCAmelCase							,       lowerCAmelCase												=  common_inputs['''input_ids'''].shape
						# Not using the same length for past_key_values
						lowerCAmelCase												=  seqlen + 2
						lowerCAmelCase												=  (
						    batch,
						    self.num_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // self.num_attention_heads,
						)
						lowerCAmelCase												=  [
						    (torch.zeros(SCREAMING_SNAKE_CASE_			), torch.zeros(SCREAMING_SNAKE_CASE_			)) for _ in range(self.num_layers			)
						]

				lowerCAmelCase												=  common_inputs['''attention_mask''']
				if self.use_past:
					lowerCAmelCase												=  ordered_inputs['''attention_mask'''].dtype
					lowerCAmelCase												=  torch.cat(
					    [ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,dtype=SCREAMING_SNAKE_CASE_			)]      ,dim=1			)

				return ordered_inputs






			@property
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	int:





				"""simple docstring"""


				return 13




"""simple docstring"""






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD




def __magic_name__	(				_lowerCamelCase:		Optional[Any]			)					->						Dict:

	'''simple docstring'''




	def wrapper(*_lowerCamelCase:		Any,					**_lowerCamelCase:		Union[str, Any]			):
		lowerCAmelCase												=  timeit.default_timer()
		lowerCAmelCase												=  func(*_lowerCamelCase,					**_lowerCamelCase			)
		lowerCAmelCase												=  timeit.default_timer() - starttime
		return delta

	lowerCAmelCase												=  func.__name__

	return wrapper




def __magic_name__	(				_lowerCamelCase:		dict,					_lowerCamelCase:		List[Any]=100,					_lowerCamelCase:		int=None			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  []
	lowerCAmelCase												=  seq_shapes or {}
	for i in range(_lowerCamelCase			):
		lowerCAmelCase												=  {}
		for col_id, (k, v) in enumerate(features.items()			):
			if isinstance(_lowerCamelCase,					_ArrayXD			):
				lowerCAmelCase												=  np.random.rand(*v.shape			).astype(v.dtype			)
			elif isinstance(_lowerCamelCase,					datasets.Value			):
				if v.dtype == "string":
					lowerCAmelCase												=  '''The small grey turtle was surprisingly fast when challenged.'''
				else:
					lowerCAmelCase												=  np.random.randint(10,					size=1			).astype(v.dtype			).item()
			elif isinstance(_lowerCamelCase,					datasets.Sequence			):
				while isinstance(_lowerCamelCase,					datasets.Sequence			):
					lowerCAmelCase												=  v.feature
				lowerCAmelCase												=  seq_shapes[k]
				lowerCAmelCase												=  np.random.rand(*_lowerCamelCase			).astype(v.dtype			)
			lowerCAmelCase												=  data

		dummy_data.append((i, example)			)

	return dummy_data




def __magic_name__	(				_lowerCamelCase:		Tuple,					_lowerCamelCase:		Tuple,					_lowerCamelCase:		Union[str, Any]=100,					_lowerCamelCase:		List[Any]=None			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  generate_examples(_lowerCamelCase,					num_examples=_lowerCamelCase,					seq_shapes=_lowerCamelCase			)

	with ArrowWriter(features=_lowerCamelCase,					path=_lowerCamelCase			) as writer:
		for key, record in dummy_data:
			lowerCAmelCase												=  features.encode_example(_lowerCamelCase			)
			writer.write(_lowerCamelCase			)

		lowerCAmelCase							,       lowerCAmelCase												=  writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""			)

	lowerCAmelCase												=  datasets.Dataset.from_file(filename=_lowerCamelCase,					info=datasets.DatasetInfo(features=_lowerCamelCase			)			)

	return dataset


